Well, no one can understand it in a 6 min. video. It doesn't even show the formal definition of Big-O neither how to prove its theorems and properties.
Idk man there's something about your presentation and the colors you use that grabs my attention and now I'm actually understanding these concepts. Thanks Bro!
Man, really thank you!!! I'm just learning for my Data Structures and Algorithms exam next week on my Uni, and Big-O was one of a few things, that I couldn't fully understand. Thanks to you now I understand it clearly
I made a summary for this lesson in the same way that Bro uses and I would like to share it with you, bros public class BigONotation {
/** * Big O Notation (how code slows as data grows): * it describes the performance of an algorithm as the amount of data * increases. * * it is machine independent but we are focusing on the "number of steps" to * complete an algorithm. * * examples of Big O notations: * O(1) * O(n) (n = amount of data) * O(log n) * O(n^2) * ... */
/** * concrete example: * addUp1() method will add up to a certain number (n). * * ex: * if n = 3 -> sum = 0 + 1 + 2 + 3 -> sum = 6. * here, the number of steps is 4 because we have one operation * (sum + i) repeated 4 times (n
Cool Bro! Great to see data structures and algorithms here. Please, more on these. Your channel is getting better and better. Subscribed!. Muchos saludos 🤙
Just as I'm getting introduced to this topic on the third semester of my Software Engineering degree in a course called Algorithms & Data Structures, I get recommended this video! Thanks, Bro Code!
Yoooo, My favorite comp sci. channel is back at it again Have you looked into rust at all? I’ve just started diving into the documentation, and I gotta say, it’s so much better than anything else I’ve used previously
For anyone wondering, O(√n) is between O(n) and O(log n). It also has a cousin O(√(n)/2) which is literally 2 times smaller even in the worst case scenario, it's important to read non simplified O notation when calculating total time (not general complexity) for your specific algorithm.
I jus wanna let u know that I'm highly addicted to your channel (after java beginner playlist)and I badly want u to complete DSA asap before facing placements Keep up the good work broman 😂
Man, thank you. I watched about 2 hours of my teacher talking about it, and in the end, I didn't even know how to tell the big O of my own algorithm, now, 10 minutes later, I understood it with a 6 min yt video
by looking at the graph can someone tell me until what value of n should I use in each O(n)? for example if n=100000000000 it's better to acheive a solution that has o(1) but if n=1 it seems that O(n) is better
bro i was a hater for learning bigO notation before watching your video. 😡 cause i cant understand that much.😬 you made me understand this bro. 😘 have you uploaded the "travelling salesman problem" video?🤨
im learning this thing but i have no idea about anything in CS my major doesnt have CS - what should i study before this so i get a basic understanding?
Good thing our professor needed 5 hours to explain that graph...
College is a scam but unfortunately we gotta do it lmfao
Mine explained it in 5 minutes so no one understood it (lol)
At least he came to a conclusion at the end
Cold, crushing grip of academia got you too?
Well, no one can understand it in a 6 min. video.
It doesn't even show the formal definition of Big-O neither how to prove its theorems and properties.
Here too, lol. I didn't understand a single thing, and nobody else did either @@noamrtdthesorcerer733
Those 6 minutes were more useful than 6 months of lectures. Thanks
It's preposterous that you can make everything this simple and smoothly learnable. Thx a lot for real
Idk man there's something about your presentation and the colors you use that grabs my attention and now I'm actually understanding these concepts. Thanks Bro!
Man, really thank you!!! I'm just learning for my Data Structures and Algorithms exam next week on my Uni, and Big-O was one of a few things, that I couldn't fully understand. Thanks to you now I understand it clearly
The guy needs to be seriously appreciated!
That was just an amazing video. Keep up the hardwork and effort you put into your videos.
I kinda somewhat get Big O notation now on a high level. that graph helped so much. Google in 3 years here I come!
I made a summary for this lesson in the same way that Bro uses and I would like to share it with you, bros
public class BigONotation {
/**
* Big O Notation (how code slows as data grows):
* it describes the performance of an algorithm as the amount of data
* increases.
*
* it is machine independent but we are focusing on the "number of steps" to
* complete an algorithm.
*
* examples of Big O notations:
* O(1)
* O(n) (n = amount of data)
* O(log n)
* O(n^2)
* ...
*/
/**
* concrete example:
* addUp1() method will add up to a certain number (n).
*
* ex:
* if n = 3 -> sum = 0 + 1 + 2 + 3 -> sum = 6.
* here, the number of steps is 4 because we have one operation
* (sum + i) repeated 4 times (n
Thank you bro !!!!
Thank you so much Bro
Thank you bro! I am in love with you for this
such a goat fr bro
Tried it, addUp1 is faster compare to addUp2.
addUp2 is only fast if there are more numbers/steps whilst
addUp1 is fast if it is less numbers/steps
Cool Bro! Great to see data structures and algorithms here. Please, more on these. Your channel is getting better and better. Subscribed!. Muchos saludos 🤙
Please keep making more videos about this it helps for interviews thanks bro
I just discovered this channel and goes through the python course I must say...... U deserve 🙏🙏🙏🙏🙏
"Prays" lmao
Just as I'm getting introduced to this topic on the third semester of my Software Engineering degree in a course called Algorithms & Data Structures, I get recommended this video! Thanks, Bro Code!
The easiness of this man's explanation is incredible
Thank you! Great code examples to demonstrate the "steps" it takes. :D
this man is the plug!
bro is on the way to 100k 🥳
really looking forward for future vids
Thank you so much bro code, I'm watching your channel,it will grow bigger then your expected
Super clear and concise. Thanks bro!🎉
Thank you! This is a great foundation for me to learn more.
Yoooo, My favorite comp sci. channel is back at it again
Have you looked into rust at all? I’ve just started diving into the documentation, and I gotta say, it’s so much better than anything else I’ve used previously
You are such a great man keep it going 💞🔥
You are amazing, Bro!!
this is so easy to understand. thanks bro!
This right here is a great man
Wow. Thanks for helping me understand Big O here than the 3 weeks we spent on in class lol
Universities are about to go bye-bye
Amazing, thank you, bro!
Excellent amazing video. Thumbs up 👍 .
Great explanations! Thanks for share.
Awesome and simple, thanks a n!
Many thanks! This video is really good for beginners!
I have always enjoyed your humour, cheers and great vid
what humor?
For anyone wondering, O(√n) is between O(n) and O(log n). It also has a cousin O(√(n)/2) which is literally 2 times smaller even in the worst case scenario, it's important to read non simplified O notation when calculating total time (not general complexity) for your specific algorithm.
i love this guy i stg
I jus wanna let u know that I'm highly addicted to your channel (after java beginner playlist)and I badly want u to complete DSA asap before facing placements
Keep up the good work broman 😂
fire explanation! thanks!
this video explain well the topic. Thank you alot for your time for making this tutorial video.
wow very good explaination thank you!
awesome explanation! Thanks
Thanks for these videos man
Thanks a lot for sharing all of this.
You always rock it down bro!....huge admiration to yuh !
Underrated!
This was great!
Nice explanation as usually 👍 🌸
Love your videos, brooo
Awesome overview
Great video!
Very useful. As a bonus I didn't know the sum of n is the same as n*(n+1)/2
Thanks!
Great thanks!
Thanks for your efforts
Bro code is different than other tutors xD. awesome
Awesome bro
thanks for the short explanation
Man, thank you. I watched about 2 hours of my teacher talking about it, and in the end, I didn't even know how to tell the big O of my own algorithm, now, 10 minutes later, I understood it with a 6 min yt video
PLZ MORE DSA. luv u
thanks habibi
Hey Bro!!!! Hope u are doing well. Thanks for such awesome content🔥🔥🔥
Love❤️
Nice explanation Bro!!!
Thanks my Bro!
thanks bro!
Love you bro
Expelled from the school 😂. Excelente video hasta ahorita el mejor explicado
You are the best
Asante kwa maelekozo mazuri
After like ten videos, this is the best video by far. 0(1) for sure
so Good thanks Bro Code
You're the bro
Respect bro 👊
Needed this video bro
Nice and succinct
by looking at the graph can someone tell me until what value of n should I use in each O(n)? for example if n=100000000000 it's better to acheive a solution that has o(1) but if n=1 it seems that O(n) is better
Thank you Bro
Nice class
amaaazing
cool!
Revision covered, my g
Thx Bro!!!
Thanks bro
ty
such a amazing explaination by the help of graph 🤩
Like I always say, my python hero
Thx bro
which programming language are you gonna use for this DSA course?
Legend
Thank you bro code
Lets go!
thx
You could add the precise definition of Big O notation, not only the intuition behind it
class video
nice
can you do a tutorial on webpack 5 ?
Is this videos by order, should I watch the Playlist from the top?
bro i was a hater for learning bigO
notation before watching your video. 😡
cause i cant understand that much.😬
you made me understand this bro. 😘
have you uploaded the "travelling salesman problem" video?🤨
im learning this thing but i have no idea about anything in CS
my major doesnt have CS - what should i study before this so i get a basic understanding?
n! getting expelled is crazyyyyy, but I agree lol
Bro can you make tutorial for mips assembly?
this is huuge, well done man.
Have a nice day ☘️☘️☘️